Abstract
Organizations today leverage big data analytics combined with modern information and communication technology (ICT) to control workers in a largely automated manner. This phenomenon is commonly referred to as algorithmic control (AC). Research has shown that worker perceptions of AC applications can impact critical outcomes such as worker resistance, continuance intention, and well-being. Despite recent efforts to better understand these worker-level effects of AC, no systematic measures of workers’ perceptions of AC exist to date. In this study, we develop and present a scale to empirically measure AC at the worker level. In particular, following established scale development guidelines, we conceptualize AC in terms of seven distinct subforms, and derive, evaluate, and validate a final set of 33 measurement items. In doing so, our study paves the way for a much-needed, more systematic investigation of AC and its worker-level implications.
Recommended Citation
Alizadeh, Armin; Hirsch, Felix; Benlian, Alexander; Wiener, Martin; and Cram, W. Alec, "Perceived Algorithmic Control: Conceptualization and Scale Development" (2023). ECIS 2023 Research Papers. 353.
https://aisel.aisnet.org/ecis2023_rp/353